Ting-Fang Lee, PhD

My research focus primarily encompasses two areas of study. Firstly, I am dedicated to develop innovative methods and statistical methodologies tailored for the analysis of intricate data structures characterized by complex dependencies. These include but are not limited to scenarios involving repeated measurements over time, correlations between interrelated variables such as those observed between eyes in medical studies, and network-based studies. Secondly, I am interested to the application of advanced mathematical tools to machine learning algorithms. Specifically, I aim to exploit these tools to conceptualize and quantitatively measure health disparities arising from electronic medical records.

My immediate focus lies in extending the regression-based transfer learning algorithm from its current application in cross-sectional studies to longitudinal data. Transfer learning offers a promising avenue to address health disparities by leveraging knowledge from well-studied populations to improve predictive models for underrepresented groups, ultimately contributing to more equitable healthcare outcomes. This expansion will enable its application in a broader range of research contexts and scenarios including longitudinal studies.